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Impact model: ORCHIDEE

Sector
Biomes
Region
global

ORCHIDEE is one of the 8 global models following the ISIMIP2a protocol which form the base of simulations for the ISIMIP2a biome sector outputs; for a full technical description of the ISIMIP2a Simulation Data from Biomes Sector, see this DOI link: http://doi.org/10.5880/PIK.2017.002

Information for the model ORCHIDEE is provided for the simulation rounds shown in the tabs below. Click on the appropriate tab to get the information for the simulation round you are interested in.

Person responsible for model simulations in this simulation round
Jinfeng Chang: changjf@zju.edu.cn, 0000-0003-4463-7778, Zhejiang University (China)
Philippe Ciais: Philippe.ciais@lsce.ipsl.fr, 0000-0001-8560-4943, IPSL ( Institute Pierre Simon Laplace) (France)
Wenfang Xu: xuwenfangfang@163.com, Laboratoire des Sciences du Climat et de l'Environnement (France)
Output Data
Experiments: I, II, III, IIIa, IIIc, VI, VII, VIII
Climate Drivers: None
Date: 2018-12-12
Basic information
Model Version: ORCHIDEE-MICT v8.4.1
Model Output License: CC BY 4.0
Reference Paper: Main Reference: Guimberteau M, Zhu D, Maignan F, Huang Y, Yue C, Dantec-Nédélec S, Ottlé C, Jornet-Puig A, Bastos A, Laurent P, Goll D, Bowring S, Chang J, Guenet B, Tifafi M, Peng S, Krinner G, Ducharne A, Wang F, Wang T, Wang X, Wang Y, Yin Z, Lauerwald R, Joetzjer E, Qiu C, Kim H, Ciais P et al. ORCHIDEE-MICT (revision 4126), a land surface model for the high-latitudes: model description and validation. Geoscientific Model Development Discussions,None,1-65,2017
Resolution
Spatial aggregation: regular grid
Horizontal resolution: 0.5°x0.5°
Additional spatial aggregation & resolution information: ORCHIDEE will be run at the resolution of 1o*1o, while we will provide interpolated 0.5o*0.5o output
Temporal resolution of input data: climate variables: daily
Temporal resolution of input data: co2: annual
Temporal resolution of input data: land use/land cover: annual
Temporal resolution of input data: soil: constant
Input data
Observed atmospheric climate data sets used: EWEMBI
Emissions data sets used: Atmospheric CO2 concentration
Socio-economic data sets used: Historical, gridded population
Land use data sets used: Historical, gridded land use (HYDE 3.2)
Other data sets used: Land-sea mask, Drainage direction map for river routing
Climate variables: tasmax, tasmin, rlds, rhs, rsds, ps, pr
Spin-up
Was a spin-up performed?: Yes
Spin-up design: We use the 1801-1820 climate condition, pre-industry CO2 (287.14 ppm), land cover map of 1860 to do the spin-up until the soil carbon to be equilibrium. Then a simulation of 1801-1860 pre-industrial period will be run. All other runs since 1861 will follow the protocol, and will be started from previous 1801-1860 run.
Natural Vegetation
Natural vegetation partition: Dynamic natural vegetation cover, with prescribed crop and pasture area.
Natural vegetation dynamics: DGVM
Key model processes
Dynamic vegetation: yes
Nitrogen limitation: no
Co2 effects: yes, Farquhar/Collatz photosynthesis
Light interception: big-leaf approach
Light utilization: Farquhar/Collatz photosynthesis
Phenology: Prognostic
Water stress: influence photosynthesis and phenology
Heat stress: aggregated degree-day sum that affect phenology
Evapo-transpiration approach: "Penman-Monteith, Milly’s correction for soil moisture stress is applied."
Differences in rooting depth: yes
Root distribution over depth: yes
Closed energy balance: yes
Coupling/feedback between soil moisture and surface temperature: yes
Latent heat: yes
Sensible heat: yes
Causes of mortality in vegetation models
Age/senescence: yes
Fire: yes, climatically dependent fire burnt area. Please see Thonicke et al., 2010, and Yue et al., 2014, 2015 for details. Briefly, the fire module of ORCHIDEE-MICT is derived from the process-based prognostic fire module SPITFIRE (Thonicke et al., 2010). In ORCHIDEE-MICT, further modifications were done (Yue et al., 2014, 2015) with 1) introducing a new factor that limits the ignition efficiency (Arora and Boer, 2005), and with 2) modified the maximum combustion completeness for fuel classes of 100 and 1000 h to be the same as mean combustion completeness by van Leeuwen et al. (2014) depending on different biomes (PFTs).
Drought: yes
Insects: no
Storm: no
Stochastic random disturbance: no
Other: no
Remarks: for different pools and for grasslands still climate dependence leading to mortality
NBP components
Fire: yes
Land-use change: yes, all deforestrated biomass transferred to litter pools on land use change
Harvest: 1) crop C from storage organ added to annual harvest flux, remaining biomass sent to litter; 2) no forest harvest
Other processes: RH
Species / Plant Functional Types (PFTs)
List of species / pfts: Bare soil (bare); tropical broad-leaved evergreen (trbrev); tropical broad-leaved raingreen (trbrrg); temperate needleleaf evergreen (tendev); temperate broad-leaved evergreen (tebrev); temperate broad-leaved summergreen (tebrsu); boreal needleleaf evergreen (bondev); boreal broad-leaved summergreen (bobrsu); boreal needleleaf summergreen (bondsu); C3 natural grass (c3gra); C4 natural grass (c4gra); C3 winter crop (c3win); C3 summer crop (c3sum); C4 maize (c4mai); C4 other crops (c4oth); C3 pasture (c3pas); C4 pasture (c4pas)
Comments: provided by Jinfeng Chang - 16-03-2017
Model output specifications
Output format: per land area, thus when calculating global or regional total, please multiply by land area of each grid
Output per pft?: grid-cell totals provided for all variables that are also provided differentiated by PFT but for annual time step. For monthly value, please multiply PFT specific value by PFT fraction of each PFTs (16 PFTs including bare soil but not non-land type water, ice etc.)
Considerations: consider all 17 PFTs.
Land-use change implementation
Is crop harvest included? if so, how?: Yes, A fixed fraction of NPP was harvested.
Is cropland soil management included? if so, how?: No.
Is grass harvest included? if so, how?: No.
Is shifting cultivation included?: No.
Is wood harvest included? if so, how?: No.
Fire modules
Aggregation of reported burnt area: The daily output is aggregated to a monthly sum.
Land-use classes allowed to burn: Natural vegetation, ISIMIP-Pasture (managed pastures, rangeland) and urban areas are allowed to burn but not cropland. Urban Land is treated as natural vegetation.
Included fire-ignition factors: Ignitions from anthropogenic and lightning sources based on availability of fuel, combustibility of fuel (soil moisture), presence of ignition source.
Is fire ignition implemented as a random process?: No, forced by lightning flash data and anthropogenic ignitions.
Is human influence on fire ignition and/or suppression included? how?: Both human ignitions and suppressions are implicitly included in a single equation based on population density, only anthropogenic fires are surpressed.
How is fire spread/extent modelled?: Fire spread simulated as a function of fuel load, fuel compact status, fire intensity and wind speed; final fire extent is determined by an assumed mean fire size derived from fire spread rate and duration, and the number of effective ignitions.
Are deforestation or land clearing fires included?: No.
What is the minimum burned area fraction at grid level?: 0
Person responsible for model simulations in this simulation round
Jinfeng Chang: changjf@zju.edu.cn, 0000-0003-4463-7778, Zhejiang University (China)
Philippe Ciais: Philippe.ciais@lsce.ipsl.fr, 0000-0001-8560-4943, IPSL ( Institute Pierre Simon Laplace) (France)
Output Data
Experiments: historical
Climate Drivers: None
Date: 2016-02-05
Basic information
Model Version: rev3013
Model Output License: CC BY 4.0
Resolution
Spatial aggregation: regular grid
Horizontal resolution: 0.5°x0.5°
Temporal resolution of input data: climate variables: daily
Temporal resolution of input data: co2: annual
Temporal resolution of input data: land use/land cover: annual
Temporal resolution of input data: soil: constant
Input data
Observed atmospheric climate data sets used: GSWP3, WATCH (WFD), WATCH-WFDEI
Climate variables: tasmax, tasmin, rlds, wind, rhs, rsds, ps, pr
Spin-up
Was a spin-up performed?: Yes
Spin-up design: We use the 1901-1910 climate condition, pre-industry CO2 (287.14 ppm), land cover map of 1860 to do the spin-up until the soil carbon to be equilibrium. Then a simulation from 1861 to 1900 was performed with varied CO2 and land-cover/land-use change, and climate of 1901-1910 cycled. The final transient simulation of 1901-2012 was forced by varied climate, CO2 and land-cover/land-use change.
Natural Vegetation
Natural vegetation partition: Prescribe natural vegetation cover. The land cover map is derived from GLC2000, and classified according to Poulter et al., 2011. The land-cover change is derived from Hurtt et al., dataset.
Key model processes
Dynamic vegetation: no
Nitrogen limitation: no
Co2 effects: yes, Farquhar/Collatz photosynthesis
Light interception: big-leaf approach
Light utilization: Farquhar/Collatz photosynthesis
Phenology: Prognostic
Water stress: influence photosynthesis and phenology
Heat stress: aggregated degree-day sum that affect phenology
Evapo-transpiration approach: "Penman-Monteith, Milly’s correction for soil moisture stress is applied."
Differences in rooting depth: no
Closed energy balance: yes
Coupling/feedback between soil moisture and surface temperature: yes
Latent heat: yes
Sensible heat: yes
Causes of mortality in vegetation models
Age/senescence: no
Fire: for forests no mortality and fire
Drought: no
Insects: no
Storm: no
Stochastic random disturbance: no
Other: no
Remarks: for different pools and for grasslands still climate dependence leading to mortality
NBP components
Fire: no
Land-use change: yes, all deforestrated biomass transferred to litter pools on land use change
Harvest: 1) C from storage organ added to annual harvest flux, remaining biomass sent to litter; 2) no forest harvest
Other processes: RH
Species / Plant Functional Types (PFTs)
List of species / pfts: Bare soil (bare); tropical broad-leaved evergreen (trbrev); tropical broad-leaved raingreen (trbrrg); temperate needleleaf evergreen (tendev); temperate broad-leaved evergreen (tebrev); temperate broad-leaved summergreen (tebrsu); boreal needleleaf evergreen (bondev); boreal broad-leaved summergreen (bobrsu); boreal needleleaf summergreen (bondsu); C3 grass (c3gra); C4 grass (c4gra); C3 winter crop (c3win); C3 summer crop (c3sum); C3 rapeseed (c3ras); C4 maize (c4mai); C4 other crops (c4oth)
Comments: provided by Jinfeng Chang - 29-10-2015
Model output specifications
Output format: per land area, thus when calculating global or regional total, please multiply by land area of each grid
Output per pft?: grid-cell totals provided for all variables that are also provided differentiated by PFT but for annual time step. For monthly value, please multiply PFT specific value by PFT fraction of each PFTs (16 PFTs including bare soil but not non-land type water, ice etc.)
Considerations: consider all 16 PFTs.
Fire modules
Aggregation of reported burnt area: The daily output is aggregated to a monthly sum.
Land-use classes allowed to burn: Natural vegetation, ISIMIP-Pasture (managed pastures, rangeland) and urban areas are allowed to burn but not cropland. Urban Land is treated as natural vegetation.
Included fire-ignition factors: Ignitions from anthropogenic and lightning sources based on availability of fuel, combustibility of fuel (soil moisture), presence of ignition source.
Is fire ignition implemented as a random process?: No, forced by lightning flash data and anthropogenic ignitions.
Is human influence on fire ignition and/or suppression included? how?: Both human ignitions and suppressions are implicitly included in a single equation based on population density, only anthropogenic fires are surpressed.
How is fire spread/extent modelled?: Fire spread simulated as a function of fuel load, fuel compact status, fire intensity and wind speed; final fire extent is determined by an assumed mean fire size derived from fire spread rate and duration, and the number of effective ignitions.
Are deforestation or land clearing fires included?: No.
What is the minimum burned area fraction at grid level?: 0
Person responsible for model simulations in this simulation round
Patricia Cadule: patricia.cadule@lsce.ipsl.fr, Laboratoire des Sciences du Climat et de l'Environment (France)
Philippe Ciais: Philippe.ciais@lsce.ipsl.fr, 0000-0001-8560-4943, IPSL ( Institute Pierre Simon Laplace) (France)
Fabienne Maignan: fabienne.maignan@cea.fr, Laboratoire des Sciences du Climat et de l'Environment (France)
Victoria Naipal: victoria.naipal@mpimet.mpg.de, Max-Planck-Institute for Meteorology (Germany)
Jan Polcher: jan.polcher@lmd.jussieu.fr, Institut Pierre Simon Laplace (France)
Nicolas Vuichard: vuichard@lsce.ipsl.fr, Laboratoire des Sciences du Climat et de l'Environment (France)
Output Data
Experiments: historical, rcp26, rcp60, rcp85
Climate Drivers: None
Date: 2013-12-17
Fire modules
Aggregation of reported burnt area: The daily output is aggregated to a monthly sum.
Land-use classes allowed to burn: Natural vegetation, ISIMIP-Pasture (managed pastures, rangeland) and urban areas are allowed to burn but not cropland. Urban Land is treated as natural vegetation.
Included fire-ignition factors: Ignitions from anthropogenic and lightning sources based on availability of fuel, combustibility of fuel (soil moisture), presence of ignition source.
Is fire ignition implemented as a random process?: No, forced by lightning flash data and anthropogenic ignitions.
Is human influence on fire ignition and/or suppression included? how?: Both human ignitions and suppressions are implicitly included in a single equation based on population density, only anthropogenic fires are surpressed.
How is fire spread/extent modelled?: Fire spread simulated as a function of fuel load, fuel compact status, fire intensity and wind speed; final fire extent is determined by an assumed mean fire size derived from fire spread rate and duration, and the number of effective ignitions.
Are deforestation or land clearing fires included?: No.
What is the minimum burned area fraction at grid level?: 0




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